Astronomers have detected more than 3000 planets beyond our solar system, and just a couple weeks ago they discovered an Earth-like planet in the solar system next door. Most--if not all--of these worlds are unlikely to harbor life, but what if we put it there? In an essay published last month in Astrophysics and Space Science, theoretical physicist Claudius Gros of Goethe University Frankfurt in Germany suggests we do just that. His proposed Genesis Project would send artificially intelligent probes to lifeless worlds to seed them with microbes. Over millions of years, they might evolve into multicellular organisms, and, perhaps eventually, plants and animals.

Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. Finding the right event labels to enable application of process mining techniques is however far from trivial, as simply using the triggering sensor as the label for sensor events results in uninformative models that allow for too much behavior (overgeneralizing). Refinements of sensor level event labels suggested by domain experts have shown to enable discovery of more precise and insightful process models. However, there exist no automated approach to generate refinements of event labels in the context of process mining. In this paper we propose a framework for automated generation of label refinements based on the time attribute of events. We show on a case study with real life smart home event data that behaviorally more specific, and therefore more insightful, process models can be found by using automatically generated refined labels in process discovery.

VentureBeat sat down with Phil Libin, managing director at venture capital firm General Catalyst, to get his take on the state of the bot ecosystem. It was just a year ago that the former Evernote CEO joined General Catalyst as a partner. Since then, chatbots and conversational interfaces have roared into the technology mainstream, with hundreds of companies attracting more than 4 billion in funding to make thousands and thousands of bots and conversational UIs for platforms like Messenger, Slack, Alexa, and Allo, to name just a few. In April, Libin made his first investment in Begin, a stealth bot startup. And a few months later, Libin announced investments in Growbot and Butter.ai,

A Japanese team comprising Tokyo-based startup ispace and Tohoku University expects to complete its moon-bound robot contestant for Google Inc.'s exploration contest by next January. Hakuto will compete against 15 other teams from around the world to win the Google Lunar X Prize, the world's first private-sector competition to explore the lunar surface. The teams will send probes developed with money and technology from the private sector to the moon, get them to travel 500 meters or farther on its dusty surface, and transmit 360-degree images back to Earth. The first team to complete the mission will win the grand prize of 20 million. While the deadline for completing the mission is the end of 2017, none of the teams has landed a probe there yet.

Sequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners. The main challenge of a portfolio planner is to define which base planners to run, to assign the running time for each planner and to decide in what order they should be carried out to optimize a planning metric. Portfolio configurations are usually derived empirically from training benchmarks and remain fixed for an evaluation phase. In this work, we create a per-instance configurable portfolio, which is able to adapt itself to every planning task. The proposed system pre-selects a group of candidate planners using a Pareto-dominance filtering approach and then it decides which planners to include and the time assigned according to predictive models. These models estimate whether a base planner will be able to solve the given problem and, if so, how long it will take. We define different portfolio strategies to combine the knowledge generated by the models. The experimental evaluation shows that the resulting portfolios provide an improvement when compared with non-informed strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014.

The output of this task is a series of predictions about binaries' potential maliciousness and relationships to known malware families. These predictions are validated against outside intelligence.

Who will remain a loyal customer and who won't? Which messages are most effective with which segments? How can customer value be maximized? This book supplies powerful tools for extracting the answers to these and other crucial business questions from the corporate databases where they lie buried. In the years since the first edition of this book, data mining has grown to become an indispensable tool of modern business.

Meet the octobot, a unique new creation out of Harvard that the university says could be the first step in a new kind of bot. This totally soft robot has eight movable legs, just like its flesh-and-blood marine cousin, which was the inspiration for the device. Created partially through 3-D printing, the octobot is autonomous, according to Harvard's John A. Paulson School of Engineering and Applied Sciences. Its power source is hydrogen peroxide, which together with a catalyst of platinum, creates gas that travels into the bot's appendages to move them. "One long-standing vision for the field of soft robotics has been to create robots that are entirely soft, but the struggle has always been in replacing rigid components like batteries and electronic controls with analogous soft systems and then putting it all together," Robert Wood, a professor of engineering and one of the lead researchers behind the octobot, said in a statement.

It seems as if everyone is finally talking about driverless vehicles, or AVs (autonomous vehicles) as they are commonly called. Even Tyler Brule, who normally likes to recount some unsatisfactory business travel experience, devoted a recent FT column to the subject. We know it's coming; so what can we expect from the driverless revolution? The upside to commuters is massive. Global auto accident deaths, around 1.25 million annually, will be radically reduced. Our cities will not only be safer, they'll be cleaner too.

While the current generation of industrial robots is primarily made of metal, the research community has been getting interested in the potential for soft-bodied robots. These have a number of advantages, such as being easy to customize via 3D printing and providing a flexibility that lets them squeeze through tight spaces. Many of the research demonstrations created so far, however, have required some compromises. For some iterations, this has meant the control hardware and power sources have been kept separate, connected to the robot via a tether. For other attempts, this has meant the final product is a mixture of hard and soft pieces.